A Survey of Different Text Mining Techniques

Abstract

In this section, we will provide you with a brief overview of various text mining tasks which are commonly used for analyzing large volumes of unstructured textual data. Text classification, grouping, entity extraction, fine-grained taxonomies, sentiment analysis, document summarization, and entity relation modeling are some of these activities. Text categorization involves organizing text into predefined categories based on its content. Clustering is the process of grouping similar documents together based on their intrinsic characteristics. Entity extraction involves identifying and extracting key elements such as people, places, and organizations from text. Granular taxonomies are hierarchical structures used for organizing textual data. Determining the general sentiment of a text, whether it be favorable, negative, or neutral, is the goal of sentiment analysis. Making a summary of a longer material is called document summarizing. Lastly, the act of determining the connections between various named entities that are stated in a text is known as entity relation modeling.